Autonomous vehicles

While autonomous vehicles are still experimental and nascent in many corners of the U.S., the same kind of unguided tectonic shift seen with the introduction of the automobile nearly a century ago is possible. Autonomous Vehicles: A Guidebook for Cities was created in response to cities seeking to manage and influence autonomous vehicle (AV) pilots and deployments happening on their streets, as well as cities trying to prepare for these pilots. The Guidebook offers considerations, tools, and examples of various ways to manage effectively autonomous vehicle deployments.

This report looks at the potential impacts autonomous vehicle deployment could have on parking demand and how that might impact urban development. The study focused on three distinct areas of San Francisco. The research found that, contrary to headlines about AV impacts on parking, achieving large reductions in parking demand based on AV deployment will not be easy. To achieve significant parking reductions, AVs would need to be shared (not privately owned), pooled (riders willing to pick up other passengers along the way), have widespread geographic deployment (across entire metropolitan areas), and would need to complement high-capacity transit. Without all or most of these factors, parking demand may only by marginally impacted by AV deployment. The study also found that if parking demand could be reduced, different areas of the city would see quite different results. While many areas in San Francisco would see minimal development impacts as parking is not currently a significant driver or limiter of development, more auto-dominated areas could see substantial impacts if parking demand could be reduced by more than 40%. This raises interesting questions of how these levels of parking demand reduction might impact more auto-dominated and suburban areas throughout the country. This research was funded by Waymo.

The Knight Autonomous Vehicle (AV) Initiative is a multi-year collaborative effort between the Urbanism Next Center at the University of Oregon, Cityfi, the cities of Detroit, Pittsburgh, and San José, and Miami-Dade County (the “cohort”) to pilot and learn about automated mobility technologies today to shape the future of deployment tomorrow. This cohort partnered with Kiwibot to learn more about a new technology—sidewalk delivery robots. Through this partnership, Kiwibot tested different use cases and collaborated on community engagement opportunities in each locale. Given the proliferation of bills being passed by state legislatures legalizing deployment of personal delivery devices (PDDs) or sidewalk robots, and the increased delivery demand due to the pandemic, the pilots were well timed to able to meaningfully inform the cohort cities about the potential benefits and challenges of sidewalk delivery robots.

This policy brief summarizes some of the key findings from a comprehensive literature review (submitted for publication) on the impact of shared mobility services and GHG emissions.

Microtransit—shared transportation that offers dynamic routing and scheduling to efficiently match demand—is emerging as an ally to fixed-route services. However, its positive impacts are too often constrained by the politics and economics imposed by existing transit infrastructure. This paper proposes a solution that ‘‘flips transit on its head.’’ By rapidly prototyping microtransit services across cities and analyzing supply-demand mismatches, it is possible to launch truly data-driven transit services. To illustrate the framework, a unique dataset generated from a year of Dallas Area Rapid Transit’s GoLink service, one of the largest ondemand microtransit services in North America, is used. Mapping and machine learning are combined to empower planners to ‘‘join the dots’’ when (re)designing fixed-route transit lines. It is shown that microtransit should not simply fill in the gaps left by inefficiently scheduled bus routes: by incorporating it fully into their planning processes, cities and transit agencies could dramatically reverse the fortunes of public transit.

This paper synthesizes and reviews all literature regarding autonomous vehicles and their impact on GHG emissions. The paper aims to eliminate bias and provide insight by incorporating statistical analysis.